Mark Werremeyer, Raytheon; Sheldon Clark, Raytheon; Jared Dorny, Raytheon; Vince Pascente, Raytheon
Keywords: Space Situational Awareness, Sensor, Network, Cloud, Data, Intelligence
Abstract:
The continued proliferation of new technologies, operators, and capabilities in the space domain has created a challenge in providing space situational awareness (SSA) intelligence and services. Specifically, the evolution of small satellite technologies over the last ten years from conceptual demonstrations to operational mega constellations has dramatically increased the complexity of the SSA mission. 2018 alone saw more than 100 successful launches with 450+ known payloads deployed, including over 60 payloads alone on the December 2018 SSO A mission. Launches in 2019 and beyond are expected to exceed this as multiple commercial mega constellations come online and operational. New capabilities and approaches to space surveillance must be employed to not only handle the increasing volume of space based objects, but to also provide timely and accurate intelligence to stakeholders. Raytheon proposes to use a variety of low cost, geographically dispersed, ground based sensors to augment the Space Surveillance Network (SSN). Leveraging the efficiencies and capabilities of low cost sensor hardware combined with near global data networking options, the SSN can provide broader monitoring services of the space domain, better prioritization tasking of larger assets, and more data backed intelligence of the space operational picture.
To help address this difficult problem, Raytheon proposes a low cost sensor network that employs scalable, distributed architectures along with cloud services to monitor space based phenomena. This concept considers the use of high performance computing (HPC) and edge processing devices to process data at the sensor and reduce network burden across many disperse geographic locations. Network connectivity from a site location can also allow the system to move raw sensor data back to a centralized cloud storage for more advanced processing into concise intelligence data products for analyst and operator consumption. HPC and edge devices can be further leveraged to perform selective machine learning services on both raw and processed data to further reduce network load or produce intelligence products at the collection source.
Data fusion of various sensing data with other sources such as more traditional optical and radar products would provide additional secondary validation of spacecraft behavior against predicted or expected behaviors, for example expected orbit parameters or transmission frequencies. Cloud based storage of collected sensor data could be used for either real time, as available, or as needed cloud processing algorithms for more specialized analyses. Real time understanding of the operational space RF environment can provide invaluable intelligence to SSA and ultimately be utilized for the security of important space based assets. Building out and understanding a more complete space operational picture will be key to the long term safety and protection of the space domain. This paper will present and detail Raytheons approach and recent efforts to implement this new sensor based SSA solution.
Date of Conference: September 17-20, 2019
Track: Optical Systems & Instrumentation